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FDA

Drug Approvals:

Time Is Money!

Andreas Sturm*

University of Regensburg, Germany,

Michael J. Dowling**

University of Regensburg, Germany

and

Klaus Röder***

University of Regensburg, Germany

We investigated the stock price behavior of publicpharmaceutical and biotechnologycompanies upon approval of a drug by the Food and Drug Administration (FDA). Using eventstudy methodology, we examine the reaction caused by the approval, separating it from theasset price movements caused by other factors such as market and industry effects. The resultsare then used to validate the model developed in this article as an alternative to the explanationsgiven by Sharma and Lacey (2004). The results of this study support the Efficient MarketHypothesis, i.e.

that the market reacts to the new information

quickly and clearly.

*

Dr. Andreas Sturm

is currently a consultant in the private equity industry. After completing his degree in BusinessSciences at the University of Regensburg, and his MBA at the MSU Kentucky, USA in 1998,

Dr. Sturm worked as aproject manager for DAB in Munich and Selftrade UK in London for four years. He subsequently returned to theUniversity of Regensburg in 2003, where he completed his doctorate on the effects of drug approval on the evaluationof biotech and pharmaceutical firms, as part of the EXIST Hightepp program.

**

Prof. Dr. Michael Dowling

is Professor for Innovation and Technology Management at the University ofRegensburg. He received his Ph.D. in Business Administration from the University

of Texas at Austin in 1988. Hisresearch interests include the strategic management of technology, especially in the telecommunications industry, hightechnology entrepreneurship, and the relationships between technology, public policy and economic development.

***

Prof. Dr. Klaus Röder

was named to the Professorship for Financial Services at the University of Regensburg in2004. Previously he had been Professor of Finance at the WWU Münster. Prof. Röder studied at the University ofAugsburg (BusinessSciences), then continued there as a research assistant at the Chair of Statistics for a total of nineyears. His research interests include empirical finance and private finance.

FDA Drug Approvals: Time Is Money!

(Sturm, Dowling & Röder)

24

Introduction

Thepurpose

of this article is to investigate the stock pricebehavior

of exchange listedpharmaceutical

and biotechnology companies at and around the time of the approvalof a drugby the Food and Drug Administration (FDA).

Usingevent

study methodologywe try

to extractthe reaction caused by the drug approval, separating it from the asset price movements causedby other factors,

such as market and industry effects.

To do

so we rely on the

Efficient MarketHypothesis

formulated byFama(1970, p. 383)

implying

that “security prices at any time‘fullyreflect’

all available information.” Thus, any new information should be reflected in the stockprices immediately.

In general, the drugdevelopment process can be split up into five different stages: The preclinical phase, the first,second and third clinical study, and the NDA submission phase. In the preclinical phase,compounds are tested in vitro, i.e. in cells and on animals. If the results are positive, thecompany then decides to proceed with further tests. To do so, the company is obliged by PublicLaw 87-781 to file an Investigational New Drug Application (IND) with the FDA. If thecompany does not receive a clinical hold within 30 days of filing the IND, it can start with theclinical trial phase. In the first clinical study, the drug is tested on around 100 healthy testpersons. If these tests are successful, the company can start with the second clinical study, inwhich the drug is tested on 200-300 test persons with the illness the drug is designed to address.In the third clinical study, tests are conducted on a much larger group of up to several thousandpatients. Once the company determines that the results prove the drugs’ effectiveness, it cancompile the collected data to then file a New Drug Application (NDA) or a Biological LicenseAgreement (BLA) with the FDA.1

The

NDA submission phase is the last step resulting inapproval or rejection of the drug for marketing in the United States.

This drug development process takes a very long time. From the beginning of thepreclinical phase to approval of a drug it takesanaverageofabout 12 years.2

Figure1

showshow much time it takes on average to pass through the different stages.

Figure 2 shows that out of 250 drugs entering preclinical trials only one gets approval.Considering all drugs entering the clinical phases, only 1 out of 5 drugs will make it toapproval.

There are several estimates concerning the financial resources needed to bring a drug tomarket.3

due to low success rates. The remainder, US$ 399million,was for costs of capital using an average Return on Equity of 11%.

Once a drug is approved, it is patent protected for 17 years from the day the companyapplied for the patent, which usually is done when the drug has been identified (end ofpreclinical testing). Considering the time needed to get drug approval, only 10 years ofeffective patent protectiontypicallyremain. During that time the company must recover its

costs and make its profits, because after a patent expires other companies will soon offer ageneric version at a lower cost, and cash flow will decrease quickly.

Drug companies are always in needof new revenue sources to offset the reduction incash flow due to drug patent expirations. Therefore, one of the biggest challenges for biotechand pharmacological companies is to invest the positive cash flows from successful drugs innew drug developments and simultaneously manage the development process efficiently toobtain approvals for new drugs. Therefore, the moment of approval is a very importantmilestone in the history of a company, recognizing the past research efforts and assuring amonopoly to market the developed drug for a certain period.

So far only fourcase

studies have been published concentrating on the biotech andpharmaceutical industries.Bosch(1994)

investigated the FDA decisions published by theWallStreet Journal

from 1962 to 1989.Bosch(1994)

found significant reactions for t=-1 and t=0(the day before and the day of publication respectively).

Deeds et al.(2003)

investigated the effect of drug rejections on the applicant company.Covering the time period from 1992 to September 2002, they wereable to identify 55 drugrejections and found a strong abnormal reaction to the event of-20%

on average, stronglysupporting the existence of negative abnormal returns to the event.

Sharma and Lacey(2004)

analyzed the effect of both approvals and rejections ofpharmacological drugs by the FDA. Their sample of “approvals” included 344 drugs and thesample of “rejections” included 41 drugs. They found that both the “approval” and the“rejection” events were efficiently incorporated into the stock price of the firms, showingstrong positive abnormal returns for approvals and strong negative abnormal returns forrejections. The reaction to approval was significant for the days t=-1, t=0 and t=1.Nosignificant reactions were observed

before or after this period. The same results were found forrejections.

Furthermore, the average reaction to approval in t=-1 to t=1 was 1.56%, comparedto-21.03% for rejection.

These resultsthereforeshowed that

rejections produce much greaterfinancial losses than the gains attributable to approvals.

Sarkar and de Jong(2006)

investigated

announcement effects at four points in theFDAreview process and how investors react.

Their sample included both large and smallpharmaceutical firms, but they did not make a distinction between biotechnology andtraditional pharmaceuticals.

In

regard to the ‘approval’ event they observed statistically

significant positive reactions on the event day and the day after. Inthecase of rejection a clearnegative reaction to the announcement on the event daywas registered.

In the following section, we will give an overview of the data used in ourstudy andofhow we analyzed them. In Section 3 we will first test whether market efficiency in its semi-strong form can be assumed. Second, we will present our valuation model, and derive and testtwo hypotheses. Thefinal

section summarizes our findings.

I.

Data and Methodology

A.

Data

Pharmaceutical drug approvals

In the time period 1985 to 2004, all pharmaceutical drug approvals defined as NewChemical Entities by Tuft´s CSDD were identified. In total, the FDA approved 487 drugsduring this 20-year period. These 487 drugs were developed by 93 different companies. As

shown inTable I, 218 drugs from 47 companieswerefinallyincluded in

thecase

study.

In thecase of S&P Composite Index, Datastream only delivered the index starting fromJanuary 1,1988.Therefore, another 22 approvals had to be excluded for the analysis using the S&PComposite Index in the market and index model.

FDA Drug Approvals: Time Is Money!

(Sturm, Dowling & Röder)

26

Biotech drug approvals

Biotech approvals in the context of this paper are defined as drugs listed as “ImportantBiological Drugs” in the annual summary of “The Pink Sheet”.6

In the case of S&P CompositeIndex, Datastream only delivered the index starting fromJanuary 1,1988. Therefore,a further

approval had to be excluded for the analysis using the S&P Composite Index in themarket andindex model.

The sample size is shown inTable II.

Stock market data

The relevant datafor each companywere retrieved from theDatastreamdatabase. In thecase of stock listings in currencies other than US dollars, the data were converted into USdollars using the exchange rates provided by Datastream.

B.

Methodology

Twoeventstudies were conducted using the Market Model and the Index Model. Forthe Market Model, the SNP Composite was used as Market Index and the necessarycoefficients were estimated for each drug approval by running OLS regressions

separately. Forthe Index Model either the World DS Biotech Index or the World DS Pharmaceutical Indexwas used as a benchmark. Details about theeventstudymethodology deployed are described inAppendix 1. The Index Model and Market Model were both used to calculate ARs and APIs forthe Biotech Approvals and the Pharmacological Approvals.

II.

Empirical Results

A.

Market Efficiency

Similar toSharma and Lacey(2004), we tested market efficiency with regard to theapproval event. In addition to the pharmacological approvals we also added

the biotechapprovals to see if therewere any differences in the reaction patterns between biotechapprovals and pharmacological approvals. In contrast toSharma and Lacey(2004)

andSarkarand de Jong(2006),

we followed the results found byBoehmer et al.(1991)

suggesting the useof varying standard deviations for each day in the eventwindow, since that improved

therobustness of the t-statistics. The following hypothesis was tested:

H 1: The approval event results in a positive abnormal return for the applicant company.

On a daily basis, highly significant abnormal returns7

can be observed in t=-1 forbiotech andt=-2 and t=1 forpharmacological approvals.

The API also shows highly significant positiveabnormal returns starting from day-1 for the biotech approvals and 2 days later (t=1) for thepharmacological approvals.

InTables

III

andIV

we present the results:

In Figure 3, one can see that the reaction to the approval happens within a few daysof

the approval. Thereafter no large changes are shown.

6

The Pink Sheet is a well-known monthly biotech/pharmaceutical publication summarizing the most importantdevelopments in the drug discovery process. In the annual summary the Pink Sheet publishes a list of “ImportantBiological Drugs”. Due to the lack of other objective criteria to sort out biological approvals (the FDA does notpublish sufficient data) we felt comfortable using these annual summaries.

7

The asterisks stand for different significance levels: * for alpha=0.10, ** for alpha=0.05 and *** for alpha =0.01.

on a daily basis show highly significantabnormal returns in t=-3, t=-1 and t=0 for the biotech approvals, whereas for pharmacologicalapprovals a significant abnormal return can only be registered at t=1. With a p

value of 0.0017,however, it is highly significant.

For the biotech approvals, the API also shows highly significant positive abnormalreturns starting from day-1 (seeTable

VI). Pharmacological approvals yield similar

results

although not that strong

(seeTableVI): The Market Model also shows significant abnormalreactions for the periods t=-2 to t=2 (biotech) and t=-2 to t=1 (pharmacological).

It can bestated that the Index Model and the Market Model deliver very similar results both stronglysupporting H1.

We have chosen a more conservative approach thanSharma and Lacey(2004)

to takeaccount of confounding events, i.e. excluding more companies from the original sample. Withsamples of 68 for biotech and 218 for pharmaceutical, the number of approvals entering thestudy is still sufficiently large. By including the biotech approvals, we were able to extend theresearch to the biotech industry. Comparing the results with the findings ofSharma and Lacey(2004)

we come up with very similar results for the abnormal returns. Calculating the simplesums of the abnormal returns for the period from t=-3 to t=3 for the pharmaceutical approvalsas an approximation,Sharma and Lacey(2004)

Taking a closer look at the Sharma and Lacey results, we found that they left thestandard deviation unchanged at approximately 0.02261 (our estimation from their data),whereas our results suggest quite substantial changes in the standard deviation, especiallyduring the approval day. Since the change mainly occurs in t=0, it is very probable that this iscaused by the approval itself. Therefore, we feel more comfortable controlling for the standarddeviation changes by using the test statistics presented in Appendix 1. However, if we use thesame constant standard deviation asSharma and Lacey(2004), we find very similar results (asshown inTable VIIcolumn `Statistic adj`). The differences in the example above become verylarge in t=0, since we record an increase in standard deviation from 0.025 to 0.061. Using themethod employed bySharma and Lacey(2004), we would get a highly significant abnormalreturn (p=0.0068), whereas our method and

data do not show any relationship.

8

Webelieve

our approach of allowing changes in standard deviationto be

a moreconservative approach since we register very significant changes in the standard deviation (inmany cases a tripling), especially on t=0 and t=1. But even with standard deviation changes, westill show significant relationships.

The second hypothesis tested was as

follows:

H 2: From t=4 to t=20 no significant abnormal return can be observed.

Biotech approvals

Like H1, H2 was tested with the Index Model with World DS Biotech Index and theMarket Model with SNP Composite Index. The API was calculated from t=4 up

to t=20. Theresults are printed in Appendix 2 and Appendix 3. For both alternative models the result isclear: There is no significant evidence of abnormal returns, either positive or negative.

8

Sarkar and de Jong (2006)

yield similar results to us, even though the two studies are not particularly comparabledue to differences in the databases.

FDA Drug Approvals: Time Is Money!

(Sturm, Dowling & Röder)

28

Pharmaceutical approvals

Pharmaceutical approvals deliver

the same results as thebiotech approvals. As shown inAppendix 4 and Appendix 5, there is no significant positive or negative abnormal return in thepost-announcement period.

Summarizing the tests for market efficiency we can state the following:

Biotech drugs

For the biotech approvals, stock prices react to the event in a semi-strong form. There isa slight tendency towards the strong form of Market Efficiency though, since a good part of theabnormal reaction already happens on day t=-1. If we take the API over t=-2 to t=2 we get apositive reaction of 2.94% (Index Model) or 2.40% (Market Model), making it highlysignificant (p=0.0084 for the Index Model and p=0.0028 for the Market Model). Expressing thereaction on t=-1 as a percentage of the API, we

show changes of 30.70% (Index Model) and41.35% (Market Model) one day before the approval becomes publicknowledge. On t=0, themarket again reacts very strongly to the approval with average abnormal gains of 0.74%(p=0.063) for the Index Model and 1.53%

(p=0.020) for the Market Model. One reason for thereaction before the approval becomes publicly known could be insider trading, of course. But itis also possible that the approval day was not correctly specified in our study–

perhaps theCEO made a public announcement concerning the approval before the actual event, forexample.

Pharmaceutical drugs

The event is efficiently incorporated into the stock prices in a semi-strong form. Wefind a highly significant abnormal return only at t=1 for both models (p=0.0023 for the IndexModel and p=0.0017 for the Market Model). For the period from t=-2 to t=2, the changes inAPIs are also highly significant at p=0.010 and p=0.026. Regarding the post-announcementwindow, there is no significant trend (defined as p<0.10) towards positive or negative abnormalreturns in either model. In summary, for the pharmaceutical approvals we find strong supportfor the existence of an efficient market in a semi-strong form.

B.

Our Valuation Model

In their interpretation,Sharma and Lacey(2004, p. 304)

concluded that “if the rewardsfor success are significantly smaller in magnitude than is the punishment for disappointment,the decision to initiate and then to continue new product development initiatives is likely to bea

difficult one for firms.” Their conclusion built on the assumption that all the approval-induced abnormal returns were covered within the event window. The results of their studywere not at all contradictory. Within the event window therewas only a clean, quick andsignificant reaction in t=-1, t=0 and t=1. All other average daily abnormal returns wereinsignificant.

However, we would like to suggest a different way of looking at the discrepancy of theabsolute abnormal return occurring between “rejections” and “approvals”. As an example, letus assume company A has a drug in development. The net present value of that drug givenapproval is US$ 1 billion.9

Since the drug has already passed the first and second phase, theprobability of approval is already fairly high at, let us say, 60%. This would suggest that the

9

The NPV is assumed to reflect also the specific risk connected to the development process by an adequatepremium in the discount rate.

drug already contributes US$ 600 million to the market capitalization of the stock.10

If thecompany passes the third phase and enters into the approval phase, the market would increasethe approval probability up to, let us say, 80 %, resulting in an abnormal increase of marketcapitalization of US$ 200 million. This example shows that one should observe a continuousincrease in the cumulated average abnormal return over the drugs which we know

will getapproval. But even drugs that are not approved but have accomplished one phase and enter thenext,

will have an increase in the expected value priced in the market capitalization. Since thehistorical approval probability for drugs entering the NDA submission is already very high(around 83%, to take the average put forward inDiMasi(2001, p. 294))

for the early ’80s toearly ’90s), a large part of the value of the drug if approved has already been anticipated andbuilt into the stock price, resulting in an abnormal return, as shown inFigure4.

Thereafter,Figure

shows the reaction to approval. If the drug is approved, there will be a quick adjustmentof the value of the drug, resulting in an increased abnormal return. After that, no abnormalreturns should be observed. In the case of rejection (see

Figure5), the market might have seen a few negative signs, but the company trying to getapproval will not jeopardize its chances by publishing negative results as long as no decisionhas been made by the regulatory body. Thus, the market might anticipate some of thedevelopment, but a large part of the rejection will still come as a surprise. Once the news of arejection comes out, the market will trade

down the stock immediately, resulting in a negativeabnormal return.

As we can see inFigure4

and

Figure5, following our argumentation it becomes clear that approvals

result in a smallerabnormal return reaction–

in absolute terms–

than rejections. Therefore, we believe that itmakes sense to take a look at the product development process as a whole. The mismatchobserved between rewards of approvals versus the punishment of a rejection is simply becauseof the build-up of abnormal returns throughout the whole development process rather than justat the end, as suggested bySharma and Lacey(2004).

Based on the argumentation above, we develop a model that we think should work wellfor drug approvals. First of all, we investigate drug approvals before and around the approvalevent since it is very difficult to filter the abnormal return of a drug throughout the wholedevelopment cycle, i.e. 12 years on average. Unfortunately, we were notable to collect data forrejections, but we believe that the results from the approvals already sufficiently support ourmodel. In the following, we concentrate our study on the time-period in the gray box withinFigure4.

Before drugs can be approved by the FDA, the company applying has to submit a NDA.The average time from NDA submission to approval of drugs is 18.2 months. On average, 83% of the drugs for which a NDA was submitted are approved.11

We believe that the marketparticipants use the average success factor to evaluate the chances of the average drug beingapproved and hence the average abnormal performance of each sponsor.

As shown inFigure6, we believe that

the market assumes some average approvalprobability for each drug, which increases over time.

The majority of this probability will be determined by historical approval probabilitiesand also drug specific factors. Conference presentations and/or published results of the drugwill change the specific approval probability for each drug in some way.12

So over time more

10

Assuming there is no unsystematic risk.

11

SeeDiMasi et al. (2003).

12

However no results from the official FDA approval process are published since that process is happening withinthe FDA and therefore is a “black box” for market participants and also the sponsors themselves.

FDA Drug Approvals: Time Is Money!

(Sturm, Dowling & Röder)

30

and more information will become available. On average, this kind of positive news flowwould mean a steady increase in the perceived probability of a drug’s success, resulting in apositive average abnormal return in the approval phase.13

However, uncertainty will still remainup to a point. This uncertainty immediately vanishes when the approval occurs. Thus,

withapproval there should be afinal positive abnormal return if the remaining uncertaintyis

sufficient to move prices.

Taking a look at the development of returns from our sample 280 days before and afterthe approval, the charts for biotech and pharmacological approvals indicate that the slopechangesaround the time of approval(Figure7

and Figure8).

From these data we can draw the conclusion that the Market Model should be used withcaution.When applying the Market Model one should be aware that the estimates of the Alphaand

Beta coefficients14

are biased in favour of a higher Alpha, leading to an underestimation(overestimation) of positive (negative) abnormal returns, especially for long-term eventstudies.15

Therefore, we control for that potential error by using only the IM

for long-termstudies (H 3).

By calculating the API with an Index Model over a two-year period around theapproval, we obtainFigure 10, API for an above-average NDA.As shown in Figure 9, there isa clear upward trend for the pharmacological approvals the year before the approval, andafterwards it remains much at the same level. The biotech approvals also show an upwardmovement of the API even though the sideward movement the year after shows a slightlydownward trend.

To cross check, we also calculated the APIs for the MM. As results we get APIs of-1.17% (pharmacological) and-9.40% (biotech) for the year before the event as opposed to19.84% and 8.22% for the IM. Giventheabove-mentioned bias through to an overestimation ofAlpha,

that difference was expected.

To see if our projection of the run-up-

caused by an increase in the expected approvalprobability-

is significant, we tested the following hypothesis:

H 3: From t=-280 to t=-3 a significant positive abnormal return can be observed.

Biotech

The IM shows no significant positive abnormal return. The absolute abnormal return of 8.22%over the period is quite large but not statistically significant. H3 is not supported for biotechapprovals.

(see Table VIII)

Pharmaceutical

Index Model with World DS Pharmaceutical Index

(seeTable IX)

With the Index Model, the API over t=-280 to t=-3 adds up to an abnormal performance of19.84 %, which is statistically significant with p=0.029.

In addition, we differentiated between drugs with a long NDA

period and those with a shortone. Our assumption is that as time passes, more information will be provided by the company.

13

It is assumed that the information flow during the FDA approval phase is independent of the speed of theapproval.

14

Assuming that the estimation period is allocated before the approval.

15

The average Alpha for daily returns, for example, was estimated at 0.06% or an annualized 16 %, clearlyoverestimating the risk free rate.

Taking only the subsample of drug approvals, news should be positive. The more positive newscomes to the market, the higher the expected success rate will be. Therefore, we believe thatthere should be a clear difference between companies whose drugs were approved below theaverage time and those whose drugs were approved above the average approval time.

In the case of above-average

approval time in10, the positive news flow wouldcontinue, resulting in an even higher approval probability priced in. Still, uncertainty remains,leaving a small abnormal return on the approval day as shown in Figure 10.

As shown inFigure11, in the

case of below-average approval time, the market will besurprised by the early approval, which has not yet been expected or built into stock prices.Intheory, this surprise effect should be reflected by a higher abnormal returnthan averagein theapproval window.

To test for differences between drugs with above-average versus below-averageapproval time, we derive the following hypothesis:

H 4: The observed abnormal returns around the approval should be larger for companieswith drugs with below-average approval time than those with above-average approval time.

If our model holds, we should be able to see significant differences.

Biotech companies

Unfortunately, the necessary data for H 4 were not available for the biotech approvals, becausethey are

not published by the FDA.

Pharmaceutical companies

InTable X, the results of drug approvals with below-average NDA times (short NDA) will becompared to those with above-average NDA times (long NDA).

Model with World DS Pharmaceutical Index

Using the

Index Model, it can be assumed that on average the short NDAs showsignificant positive APIs from t=-1 onwards. For the long NDAs, however, positive APIs arenot statistically significant. The percentage changes speak for the short NDAs where 2.44%API from t=-2 to t=3, whereas the long NDAs register only 0.42% respectively as shown inTable XI.

Testing the two groups for differences for the period from t=-2 to t=3, the short NDAshave a significantly higher API than the long NDAs (p=0.048).

As can be seen in Figure 12

andTable XII, there is clearlyahigher performance in the period between t=-3 to t=3, wherethe reaction to the approval occurs. Thereafter, no large changes in the API are to be observed.

Similar but weaker results can beachieved

whenwe apply the Market Model. The short NDAsshow a significant positive API (p=0.039) effect for the time period t=-2 to t=3, whereas thelong NDAs do not show any effect as shown inTable XIII.

The test of differences shows aweak tendency (p=0.084) for short NDAs to outperform long NDAs. The percentage differenceadds up to 1.81% in t=3.

C.

Sensitivityto

outliers

Even though we felt quite confident with above results, since they were in

line with theresults ofSarkar and de Jong(2006)

andSharma and Lacey(2004), we tested the robustness of

theabove statistics by trying to control for the effects of outliers. Due tothe larger

sample sizeFDA Drug Approvals: Time Is Money!

(Sturm, Dowling & Röder)

32

we present in the following the results for thepharmaceutical

events. The results are similar forthebiotech sample.

The distribution for the APIs for the event window is exhibited in Figure 13. As one cansee,

there isa tendency toward positive outliers with APIs of above 20%. In the samplefive

observations have APIs of above 20%, whereas no negative API below-20% can be observed.

Now the question is what would happen if onetook

the outliers out of the sample. The answeris givenin

Table XIV,

where the statistics are calculated without the API outliers above 20%and below-20% and are compared to the original sample results. The result is quite clear: Nosignificant positive API reaction remains.

Analyzing thefive

outliers, we find a simple explanation:At $4.21 billion, the averagemarket capitalization of event sponsors that produced those outliers was about one

sixth of theaverage market cap in the sample. Hence,due

to basis effects,

the API reactions will behigher

by nature, assuming the same absolute reaction to the event. Therefore,

the outliers are notreally outliers inthe

statistical sense. They just occurdue

to the wrong methodbeingapplied.Consequently,it is preferable

to investigate the absolutereaction as opposed to the relative.

Calculations of the absolute API returns are shown inTable XV. Again, no significantabnormal reaction can be observed.

For thebiotech event we obtain similar results (Table XVI): Former highly significantresults vanish almost completely. Only in t=0dosignificant reactions remain.

These resultsshow impressively the sensitivity of the applied test statistics to outliers, andcalls

into questionthe existence of abnormal reaction. It might well be that no reactionis observed

after all.

D.

Findings for our Valuation Model

Our Valuation Model shows support for H3 (testing for a significant abnormal return int=-280 to t=-3) and H4 (testing for differences in abnormal returns depending on the length ofthe NDA submission period).

H3 can be supported for the pharmaceutical approvals. From t=-280 to t=-3 an API of19.84% was registered (p=0.029). This

clearly indicates that the market prices reflectinformation obtained over time, e.g. by presentations of drug results at conferences or companypress releases (seeFigure6).

For the biotech approvals, no significant results were obtained. The reason for thiscould lie in high burn rates of money, that is, the gain within theNDA phase could be offset bya high negative cash flow. Another reason could be found in the more specialized approach ofbiotech drugs aiming for a certain segment, which does not allow above-market return even inthe case of success since the potential profits with niche products in absolute numbers are muchsmaller than those of blockbusters.16

Testing H4, in the period t=-2 to t=3 for the Index Model we find a significantly higherAPI for the group with short NDAs than for the group with long NDAs (p=0.048). For theMarket Model, we observe for the same period at least a tendency for the short NDAs tooutperform (p=0.084). Our Valuation Model explainstheseresults very well (seeFigure10

andFigure11). Therefore, we see the difference between short and long NDAs as supporting

ourmodel.

Last but not least, the sensitivity oftheabove conclusions to outliers should not beunderestimated. Asisshown, taking away the outliers leads to an almost completedisappearance ofsignificant reactions, questioning the conclusions of

studies made up to nowin that field. In general,

for the drug approvals it seems more appropriate to look at the absolute

16

In return, those niche products (orphan drugs) should see a much higher approval rate.

Because the company was not publicly listed, or insufficient data before or afterthe approval date:19

-110

Drug approvals included in the event study:

218

Table II

Biotechnology Sample Selection

Total drug approvals 1985-2004:

109

Deleted due to mergers & acquisitions:20

-12

Deleted due to drug approval within the same company:21

-8

Drugs where the sponsor was not publicly listed, or insufficient data before orafter the approval date:22

-21

Drugs included in the event study:

68

Table III

Biotech-

ARs for Index Model with World DS Biotech Index

and

World DS Pharmacological Index

Biotech

Biotech

Pharmaceutical

t

Average AR

t-Statistic

N

Average AR

t-Statistic

N

-3

-0.185%

-0.683

68

-0.010%

-0.007

218

-2

0.144%

0.491

68

0.266%

1.301

218

-1

0.902%

2.468***

68

0.103%

0.642

218

0

0.735%

1.499*

68

0.245%

0.592

218

1

0.947%

1.267

68

0.553%

2.753***

218

2

0.121%

0.261

68

0.077%

0.437

218

3

-0.493%

-1.300*

68

-0.029%

-0.149

218

17

Drug approvals were taken out of the sample if the applicant company formed part of a merger or an acquisitionactivity up to two years before and six months after the merger or acquisition was announced to preventconfounding effects.

18

A confounding effect was assumed to exist in the event of another drug approval within the same company sixmonths before or three months after each approval.

19

A minimum of data seven months before andafter the approval was required to be included in the approvalstudy.

Very similar to the methodology used by,Sharma and Lacey 2004), our study estimatedmarket model parameters using a 140-day period,day-150 to-10).

The six-day event window,day-2 through day +3) was followed by a post-announcement window,day +4 through day+20) for a check of abnormal returns out of the event window. The abnormal returntiAR,

ondayt

for securityi

in the market model was calculated as follows:

)(,,,tititiRERAR

wheretiR,

is the observed return on dayt

for securityi

and)(,tiRE

isthe expected return on dayt

for securityi. The expected return was calculated astmtiRRE,,)(

wheretmR,

is the market return and

and

are the parameters obtainedfrom the ordinary least square regressionstitmtiRR,,,

for each single securityi.

The abnormal returntiAR,

on dayt

for securityi

in the index model was simply obtained bytmtitiRRAR,,,.

In both the index and the market model, two indices were used as a reference index: The SNPComposite and the World DS Biotech in the case of biotech approvals or the World DSPharmaceutical in the case of pharmaceutical approvals.

The Abnormal Performance Indices,APIs) were calculated by cumulating the daily abnormalreturns as follows:NNRACARtiNikttKktt))1((,1,.

Test statistics:

The significance of daily ARs was determined by using an approximate Gaußtest. The teststatistic used was as follows: